graph-operations — for Claude Code graph-operations, 1C-Enterprise_Framework, community, for Claude Code, ide skills, graph_embeddings, "EntityName (TYPE): key=val", ## Конфиг, GraphChangeDetector, IncrementalGraphUpdater

v1.0.0
GitHub

Sobre este Skill

Cenario recomendado: Ideal for AI agents that need когда использовать. Resumo localizado: graph-operations helps AI agents handle repository-specific developer workflows with documented implementation details.

Recursos

Когда использовать
"граф знаний", "сущности", "связи", "entities"
"LightRAG", "GraphRAG", "graph traversal"
Построение графа, поиск по графу, entity extraction
Режимы поиска по графу

# Core Topics

for Claude Code graph_embeddings "EntityName (TYPE): key=val" ## Конфиг GraphChangeDetector IncrementalGraphUpdater
Alex1980Alex Alex1980Alex
[0]
[1]
Updated: 3/29/2026

Killer-Skills Review

Decision support comes first. Repository text comes second.

Reference-Only Page Review Score: 8/11

This page remains useful for operators, but Killer-Skills treats it as reference material instead of a primary organic landing page.

Original recommendation layer Concrete use-case guidance Explicit limitations and caution
Review Score
8/11
Quality Score
43
Canonical Locale
ru
Detected Body Locale
ru

Cenario recomendado: Ideal for AI agents that need когда использовать. Resumo localizado: graph-operations helps AI agents handle repository-specific developer workflows with documented implementation details.

Por que usar essa habilidade

Recomendacao: graph-operations helps agents когда использовать. graph-operations helps AI agents handle repository-specific developer workflows with documented implementation details.

Melhor para

Cenario recomendado: Ideal for AI agents that need когда использовать.

↓ Casos de Uso Práticos for graph-operations

Caso de uso: Applying Когда использовать
Caso de uso: Applying "граф знаний", "сущности", "связи", "entities"
Caso de uso: Applying "LightRAG", "GraphRAG", "graph traversal"

! Segurança e Limitações

  • Limitacao: Requires repository-specific context from the skill documentation
  • Limitacao: Works best when the underlying tools and dependencies are already configured

Why this page is reference-only

  • - Current locale does not satisfy the locale-governance contract.
  • - The underlying skill quality score is below the review floor.

Source Boundary

The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

After The Review

Decide The Next Action Before You Keep Reading Repository Material

Killer-Skills should not stop at opening repository instructions. It should help you decide whether to install this skill, when to cross-check against trusted collections, and when to move into workflow rollout.

Install Path

Start With Installation And Validation

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Trusted Recheck

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Workflow

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Labs Demo

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Boot Container Sandbox

FAQ & Installation Steps

These questions and steps mirror the structured data on this page for better search understanding.

? Frequently Asked Questions

What is graph-operations?

Cenario recomendado: Ideal for AI agents that need когда использовать. Resumo localizado: graph-operations helps AI agents handle repository-specific developer workflows with documented implementation details.

How do I install graph-operations?

Run the command: npx killer-skills add Alex1980Alex/1C-Enterprise_Framework/graph-operations. It works with Cursor, Windsurf, VS Code, Claude Code, and 19+ other IDEs.

What are the use cases for graph-operations?

Key use cases include: Caso de uso: Applying Когда использовать, Caso de uso: Applying "граф знаний", "сущности", "связи", "entities", Caso de uso: Applying "LightRAG", "GraphRAG", "graph traversal".

Which IDEs are compatible with graph-operations?

This skill is compatible with Cursor, Windsurf, VS Code, Trae, Claude Code, OpenClaw, Aider, Codex, OpenCode, Goose, Cline, Roo Code, Kiro, Augment Code, Continue, GitHub Copilot, Sourcegraph Cody, and Amazon Q Developer. Use the Killer-Skills CLI for universal one-command installation.

Are there any limitations for graph-operations?

Limitacao: Requires repository-specific context from the skill documentation. Limitacao: Works best when the underlying tools and dependencies are already configured.

↓ How To Install

  1. 1. Open your terminal

    Open the terminal or command line in your project directory.

  2. 2. Run the install command

    Run: npx killer-skills add Alex1980Alex/1C-Enterprise_Framework/graph-operations. The CLI will automatically detect your IDE or AI agent and configure the skill.

  3. 3. Start using the skill

    The skill is now active. Your AI agent can use graph-operations immediately in the current project.

! Reference-Only Mode

This page remains useful for installation and reference, but Killer-Skills no longer treats it as a primary indexable landing page. Read the review above before relying on the upstream repository instructions.

Upstream Repository Material

The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

Upstream Source

graph-operations

Install graph-operations, an AI agent skill for AI agent workflows and automation. Review the use cases, limitations, and setup path before rollout.

SKILL.md
Readonly
Upstream Repository Material
The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.
Supporting Evidence

Graph Operations

Когда использовать

  • "граф знаний", "сущности", "связи", "entities"
  • "LightRAG", "GraphRAG", "graph traversal"
  • Построение графа, поиск по графу, entity extraction

Режимы поиска по графу

РежимСтоимостьЛатентностьКогда
LightRAG~100 tokens<500msПростые/средние вопросы (default)
GraphRAG Local~2000 tokens2-5sСложные вопросы с контекстом
GraphRAG Global~5000 tokens5-10sТематические обзоры, map-reduce
AutovariesvariesКлассификатор → LightRAG или Full

LightRAG (Phase 38)

Поиск по embeddings сущностей/связей в Qdrant graph_embeddings collection:

  • 6694 точек (3166 entities + 3528 relations)
  • Entity text: "EntityName (TYPE): key=val"
  • Relation text: "Source -[TYPE]-> Target: key=val"
  • Auto-select: simple/moderate → LightRAG, complex/thematic → Full GraphRAG

Операции

python
1# Построение графа из чанков
2builder = GraphBuilder(extractor, graph_store, concurrency=5)
3stats = await builder.build_from_chunks(chunks)
4
5# Построение entity embeddings
6entity_builder = EntityEmbeddingBuilder(embedding_engine, lightrag_settings)
7await entity_builder.build(graph_store)
8
9# Поиск соседей
10neighbors = await graph_store.get_neighbors(entity_id, depth=2)
11
12# Поиск пути
13path = await graph_store.find_path(source_id, target_id, max_depth=5)

Конфиг

env
1GRAPHRAG__COMMUNITY_DETECTION_ENABLED=true
2GRAPHRAG__LEIDEN_RESOLUTION=1.0
3GRAPHRAG__LOCAL_SEARCH_DEPTH=1
4GRAPHRAG__GLOBAL_SEARCH_MAX_COMMUNITIES=20
5LIGHTRAG__ENABLED=true
6LIGHTRAG__ENTITY_TOP_K=10
7LIGHTRAG__RELATION_TOP_K=10
8LIGHTRAG__AUTO_SELECT_ENABLED=true
9GRAPHSTORE__PROVIDER=networkx     # networkx|neo4j

Incremental Graph Update (Phase 61)

Инкрементальное обновление графа без полной перестройки:

КомпонентКлассНазначение
Change DetectorGraphChangeDetectorСравнивает entities/relations с предыдущей версией
Incremental UpdaterIncrementalGraphUpdaterДобавляет/удаляет/обновляет только изменения

Pipeline: Re-extract entities → Change Detector (diff) → Incremental Updater (apply delta)

Экономит 80-95% времени при обновлении документа (только изменённые entity/relation).

Диагностика

СимптомПричинаРешение
Пустой графEntity extraction без результатовПроверить LLM prompt + token limits
LightRAG fallback на vectorНет entity embeddingsЗапустить entity_builder.build(graph_store)
Дубли сущностейDedup key mismatchКлюч: name.lower().strip() + entity_type
NetworkX медленный>10K entitiesИспользовать set_batch_mode(True) + flush()

Файлы

  • Base: src/pdf_framework/graph_store/base.py
  • NetworkX: src/pdf_framework/graph_store/providers/networkx_store.py
  • Neo4j: src/pdf_framework/graph_store/providers/neo4j_store.py
  • Builder: src/pdf_framework/graph_store/construction/builder.py
  • Entity embeddings: src/pdf_framework/graph_store/entity_embeddings.py
  • Change Detector: src/pdf_framework/graph_store/change_detector.py
  • Incremental Updater: src/pdf_framework/graph_store/incremental.py
  • Strategies: src/pdf_framework/search/strategies/graph_search.py, graphrag_light.py, graphrag_global.py, graphrag_auto.py
  • Extractor: src/pdf_framework/processing/extractors/entity_extractor.py

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